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Welcome! A Special R Package from Thomas Debray, PhD
Published about 1 month ago • 1 min read
Dear Reader:
Welcome to the SDAS community! I’m excited to have you on board and look forward to sharing valuable insights, tools, and resources to support your work in data science and statistics.
A bit about me
I’ve had the opportunity to work at the forefront of data science and statistics in the pharmaceutical industry, where things are moving at light speed. My background includes:
Assistant Professor at the University Medical Center, Utrecht
Principal Investigator for multiple RWE projects, including contributions to various guidelines,
Consultant providing advanced data science methodologies to pharmaceutical companies
I’m excited to share cutting-edge innovations with you, hoping they contribute to your journey in data science and evidence synthesis! 🚀
🚀 Meet SimTOST – A Smarter Way to Design Bioequivalence Trials
Staying ahead in bioequivalence trials requires the right tools. That’s why I’m thrilled to introduce you to SimTOST — an open-source R package optimized with C++ for high-performance simulations.
With SimTOST, you can:
Estimate sample size efficiently for equivalence trials
Handle multiple treatment arms and primary endpoints simultaneously
Correct for multiplicity and account for correlated endpoints
Get started today — Download SimTOST on GitHub or CRAN!
Want Hands-On Examples?
"I like to work through the code"
"I like to see examples while I'm working through the code"
Learn by doing! Our vignettes provide hands-on examples to help you master SimTOST and apply it effectively to your research
If you have questions about SimTOST, R, or evidence synthesis methodologies, let's talk! Schedule time here . I'll also be attending several upcoming industry events, if you're going to, let's meet up and chat there:
Sincerely,
Thomas Debray, PhD Founder & Owner Smart Data Analysis and Statistics B.V.
Let's talk evidence synthesis, RWE and AI - Book some time here
Handbook of Comparative Effectiveness and Personalized Medicine using RWE (2025)
Edited by Thomas Debray, R.W. Platt & L. Nguyen
A practical guide to estimating treatment effectiveness in real-world populations. Learn how to leverage real-world data (RWD)—alone or combined with other sources—to generate both overall and individualized treatment effect estimates, and more!
Optimize sample size estimation for randomized bioequivalence trials with powerful simulation capabilities.
Supports multiple correlated primary endpoints
Handles multiple treatment arms
Built-in multiplicity corrections
Optimized with C++ for fast simulations
Extensive vignettes
SimTOST is open-source, combining the flexibility of R with the speed of C++ for computational efficiency. The package has recently been released on CRAN.
Outsourcing evidence synthesis can mean high costs, long timelines, and less control. Our AI-powered tool streamlines Network Meta-Analysis, delivering HTA-compliant reports efficiently—giving you control without compromising quality.
Stay ahead with our newsletter, crafted for detail-oriented statisticians committed to making a meaningful impact. Discover advanced methods for clinical trial and real-world evidence studies, strategies to enhance transparency and reproducibility, and updates on essential trainings and workshops.
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